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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*/
package org.apache.skywalking.oap.server.core.analysis.metrics;
import java.util.Comparator;
import java.util.List;
import java.util.stream.IntStream;
import lombok.Getter;
import lombok.Setter;
import org.apache.skywalking.oap.server.core.analysis.metrics.annotation.Arg;
import org.apache.skywalking.oap.server.core.analysis.metrics.annotation.Entrance;
import org.apache.skywalking.oap.server.core.analysis.metrics.annotation.MetricsFunction;
import org.apache.skywalking.oap.server.core.analysis.metrics.annotation.SourceFrom;
import org.apache.skywalking.oap.server.core.storage.annotation.Column;
import org.apache.skywalking.oap.server.core.storage.annotation.ElasticSearch;
/**
* Percentile is a better implementation than deprecated PxxMetrics in older releases.
* This could calculate the multiple P50/75/90/95/99 values once for all.
*
* @since 7.0.0
*/
@MetricsFunction(functionName = "percentile")
public abstract class PercentileMetrics extends Metrics implements MultiIntValuesHolder {
protected static final String DATASET = "dataset";
protected static final String VALUE = "value";
protected static final String PRECISION = "precision";
private static final int[] RANKS = {
50,
75,
90,
95,
99
};
@Getter
@Setter
@Column(columnName = VALUE, dataType = Column.ValueDataType.LABELED_VALUE, storageOnly = true)
@ElasticSearch.Column(columnAlias = "datatable_value")
private DataTable percentileValues;
@Getter
@Setter
@Column(columnName = PRECISION, storageOnly = true)
private int precision;
@Getter
@Setter
@Column(columnName = DATASET, storageOnly = true)
private DataTable dataset;
private boolean isCalculated;
public PercentileMetrics() {
percentileValues = new DataTable(RANKS.length);
dataset = new DataTable(30);
}
@Entrance
public final void combine(@SourceFrom int value, @Arg int precision) {
this.isCalculated = false;
this.precision = precision;
String index = String.valueOf(value / precision);
dataset.valueAccumulation(index, 1L);
}
@Override
public boolean combine(Metrics metrics) {
this.isCalculated = false;
PercentileMetrics percentileMetrics = (PercentileMetrics) metrics;
this.dataset.append(percentileMetrics.dataset);
return true;
}
@Override
public final void calculate() {
if (!isCalculated) {
long total = dataset.sumOfValues();
int[] roofs = new int[RANKS.length];
for (int i = 0; i < RANKS.length; i++) {
roofs[i] = Math.round(total * RANKS[i] * 1.0f / 100);
}
int count = 0;
final List<String> sortedKeys = dataset.sortedKeys(Comparator.comparingInt(Integer::parseInt));
int loopIndex = 0;
for (String key : sortedKeys) {
final Long value = dataset.get(key);
count += value;
for (int rankIdx = loopIndex; rankIdx < roofs.length; rankIdx++) {
int roof = roofs[rankIdx];
if (count >= roof) {
percentileValues.put(String.valueOf(rankIdx), Long.parseLong(key) * precision);
loopIndex++;
} else {
break;
}
}
}
}
}
@Override
public int[] getValues() {
return percentileValues.sortedValues(Comparator.comparingInt(Integer::parseInt))
.stream()
.flatMapToInt(l -> IntStream.of(l.intValue()))
.toArray();
}
}